TaoLv commented on a change in pull request #14614: [MKLDNN] add quantized sum
URL: https://github.com/apache/incubator-mxnet/pull/14614#discussion_r276585921
##########
File path: tests/python/quantization/test_quantization.py
##########
@@ -213,6 +213,71 @@ def check_quantized_conv(data_shape, kernel, num_filter,
pad, stride, no_bias, q
check_quantized_conv((3, 4, 28, 28), (3, 3), 128, (1, 1), (1, 1),
True, qdtype)
check_quantized_conv((3, 4, 28, 28), (3, 3), 128, (1, 1), (1, 1),
False, qdtype)
+
+@with_seed()
+def test_quantized_elemwise_add():
+ def check_quantized_elemwise_add(data_shape, qtype):
+ if is_test_for_native_cpu():
+ print('skipped testing quantized_elemwise_add for native cpu since
it is not supported yet')
+ return
+ elif qtype != 'uint8' and qtype != 'int8':
+ print('skipped testing quantized_elemwise_add for not supported
data type')
+ return
+ elif is_test_for_gpu():
+ print('skipped testing quantized_elemwise_add for gpu since it is
not supported yet')
+ return
+
+ dataA = mx.sym.Variable(name='dataA', shape=data_shape,
dtype='float32')
+ dataB = mx.sym.Variable(name='dataB', shape=data_shape,
dtype='float32')
+ elemwise_add_fp32 = mx.sym.elemwise_add(dataA, dataB)
+ arg_names = elemwise_add_fp32.list_arguments()
+ elemwise_add_fp32_exe =
elemwise_add_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
+ if qtype == 'uint8':
+ data_low = 0.0
+ data_high = 255.0
+ else:
+ data_low = -127.0
+ data_high = 127.0
+
+ dataA_val = mx.nd.random.uniform(low=data_low, high=data_high,
shape=data_shape).astype('int32')
+ dataB_val = mx.nd.random.uniform(low=data_low, high=data_high,
shape=data_shape).astype('int32')
+ elemwise_add_fp32_exe.arg_dict[arg_names[0]][:] = dataA_val
+
+ elemwise_add_fp32_exe.arg_dict[arg_names[1]][:] = dataB_val
+
+ output = elemwise_add_fp32_exe.forward()[0]
+
+ qdataA = mx.sym.Variable(name='qdataA', shape=data_shape, dtype=qtype)
+ qdataB = mx.sym.Variable(name='qdataB', shape=data_shape, dtype=qtype)
+ min_dataA = mx.sym.Variable(name='min_dataA')
+ max_dataA = mx.sym.Variable(name='max_dataA')
+ min_dataB = mx.sym.Variable(name='min_dataB')
+ max_dataB = mx.sym.Variable(name='max_dataB')
+ quantized_elemwise_add = mx.sym.contrib.quantized_elemwise_add(qdataA,
qdataB, min_dataA, max_dataA, min_dataB, max_dataB)
+ elemwise_add_int8_exe =
quantized_elemwise_add.simple_bind(ctx=mx.current_context(), grad_req='null')
+ qarg_names = quantized_elemwise_add.list_arguments()
+ elemwise_add_int8_exe.arg_dict[qarg_names[0]][:] =
elemwise_add_fp32_exe.arg_dict[arg_names[0]].astype(qtype)
+ elemwise_add_int8_exe.arg_dict[qarg_names[1]][:] =
elemwise_add_fp32_exe.arg_dict[arg_names[1]].astype(qtype)
+ quantized_range = 127.0
+ elemwise_add_int8_exe.arg_dict[qarg_names[2]][:] = data_low
+ elemwise_add_int8_exe.arg_dict[qarg_names[3]][:] = data_high
+ elemwise_add_int8_exe.arg_dict[qarg_names[4]][:] = data_low
+ elemwise_add_int8_exe.arg_dict[qarg_names[5]][:] = data_high
+ qoutput, min_range, max_range = elemwise_add_int8_exe.forward()
+ min_val = min_range.asnumpy().tolist()[0]
+ max_val = max_range.asnumpy().tolist()[0]
+
+ fp32_rslt = output.asnumpy()
+ int8_rslt = qoutput.asnumpy()*max_val/0x7fffffff
+ assert_almost_equal(int8_rslt, int8_rslt, atol = 1)
Review comment:
why choose atol=1?
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